| Patent application number | Description | Published |
|---|
| 20080275902 | Web page analysis using multiple graphs - A collection of web pages is modeled as a directed graph, in which the nodes of the graph are the web pages and directed edges are hyperlinks. Web pages can also be represented by content, or by other features, to obtain a similarity graph over the web pages, where nodes again denote the web pages and the links or edges between each pair of nodes is weighted by a corresponding similarity between those two nodes. A random walk is defined for each graph, and a mixture of the random walks is obtained for the set of graphs. The collection of web pages is then analyzed based on the mixture to obtain a web page analysis result. The web page analysis result can be, for example, clustering of the web pages to discover web communities, classifying or categorizing the web pages, or spam detection indicating whether a given web page is spam or content. | 11-06-2008 |
| 20080281817 | Accounting for behavioral variability in web search - The concept of variability pertains to whether users exhibit consistent search interaction patterns, for example, in terms of interaction flow or information targeted. Methods are provided for analyzing variability, and then adapting search-related functionality (e.g., processes and/or interfaces) to account for variability characteristics, for example, to account for predictable search interaction behavior. | 11-13-2008 |
| 20090106229 | Linear combination of rankers - Described herein is a system that includes a receiver component that receives first scores for training points and second scores for the training points, wherein the first scores are individually assigned to the training points by a first ranker component and the second scores are individually assigned to the training points by a second ranker component. The apparatus further includes a determiner component in communication with the receiver component that automatically outputs a value for a parameter α based at least in part upon the first scores and the second scores, wherein α is used to linearly combine the first ranker component and the second ranker component. | 04-23-2009 |
| 20090106232 | BOOSTING A RANKER FOR IMPROVED RANKING ACCURACY - A system described herein includes a trainer component that receives an estimated gradient of cost that corresponds to a first ranker component with respect to at least one training point and at least one query. The trainer component builds a second ranker component based at least in part upon the received estimated gradient. The system further includes a combiner component that linearly combines the first ranker component and the second ranker component. | 04-23-2009 |
| 20090112781 | PREDICTING AND USING SEARCH ENGINE SWITCHING BEHAVIOR - Aspects of the subject matter described herein relate to predicting and using search engine switching behavior. In aspects, switching components receive a representation of user interactions with at least one browser. The switching components derive information from the representation that is useful in predicting whether a user will switch search engines. The derived information and information about a user's current interaction with a browser is then used by a switch predictor to predict whether the user will switch search engines. This prediction may be used in a variety of ways examples of which are given herein. | 04-30-2009 |
| 20100281024 | LINEAR COMBINATION OF RANKERS - Described herein is a system that includes a receiver component that receives first scores for training points and second scores for the training points, wherein the first scores are individually assigned to the training points by a first ranker component and the second scores are individually assigned to the training points by a second ranker component. The apparatus further includes a determiner component in communication with the receiver component that automatically outputs a value for a parameter α based at least in part upon the first scores and the second scores, wherein α is used to linearly combine the first ranker component and the second ranker component. | 11-04-2010 |
| 20100318540 | IDENTIFICATION OF SAMPLE DATA ITEMS FOR RE-JUDGING - Described is a technology for identifying sample data items (e.g., documents corresponding to query-URL pairs) having the greatest likelihood of being mislabeled when previously judged, and selecting those data items for re-judging. In one aspect, lambda gradient scores (information associated with ranked sample data items that indicates a relative direction and how “strongly” to move each data item for lowering a ranking cost) are summed for pairs of sample data items to compute re-judgment scores for each of those sample data items. The re-judgment scores indicate a relative likelihood of mislabeling. Once the selected sample data items are re-judged, a new training set is available, whereby a new ranker may be trained. | 12-16-2010 |
| 20110238648 | PREDICTING AND USING SEARCH ENGINE SWITCHING BEHAVIOR - Aspects of the subject matter described herein relate to predicting and using search engine switching behavior. In aspects, switching components receive a representation of user interactions with at least one browser. The switching components derive information from the representation that is useful in predicting whether a user will switch search engines. The derived information and information about a user's current interaction with a browser is then used by a switch predictor to predict whether the user will switch search engines. This prediction may be used in a variety of ways examples of which are given herein. | 09-29-2011 |
